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[General] CT-AI Latest Test Questions - CT-AI New Dumps Pdf

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【General】 CT-AI Latest Test Questions - CT-AI New Dumps Pdf

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ISTQB CT-AI Exam Syllabus Topics:
TopicDetails
Topic 1
  • Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 2
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 3
  • Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 4
  • ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 5
  • Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 6
  • Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 7
  • ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 8
  • Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 9
  • Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Topic 10
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q116-Q121):NEW QUESTION # 116
"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION
  • A. A lack of focus on non-functional requirements testing.
  • B. A lack of similarity between the training and testing data.
  • C. A lack of focus on choosing the right functional-performance metrics.
  • D. The input data has not been tested for quality prior to use for testing.
Answer: B
Explanation:
The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.
* A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.
* The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.
* A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.
* A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.
References:
* ISTQB CT-AI Syllabus Section 4.2 on Training, Validation, and Test Datasets emphasizes the importance of using representative datasets to ensure the model generalizes well to real-world data.
* Sample Exam Questions document, Question #40 addresses issues related to data representativeness and model generalization.

NEW QUESTION # 117
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?
  • A. Exploratory testing
  • B. State transition testing
  • C. Static analysis
  • D. Equivalence partitioning
Answer: A
Explanation:
Whentesting an AI-based chat assistantfor e-commerce customers, thelack of sufficient specifications makes it difficult to use structured test techniques. TheISTQB CT-AI Syllabusrecommendsexploratory testingin such cases:
* Why Exploratory Testing?
* Exploratory testing is usefulwhen specifications are incomplete or unclear.
* AI-based systems, particularly those usingnatural language processing (NLP),may not behave deterministically, making scripted test cases ineffective.
* Thetester interacts dynamicallywith the system, identifying unexpected behaviorsnot documented in the specification.
* Analysis of Answer Choices:
* A (Exploratory testing)#Correct, as it is the best approach when specifications are incomplete.
* B (Static analysis)# Incorrect, as static analysis checks code without execution, which isnot helpfulfor AI chatbots.
* C (Equivalence partitioning)# Incorrect, asthis technique requires well-defined inputs and outputs, which are missing due toinsufficient specifications.
* D (State transition testing)# Incorrect, as state-based testingrequires knowledge of valid and invalid transitions, which is difficult with a chatbot lacking a clear specification.
Thus,Option A is the correct answer, asexploratory testing is the best approach when dealing with insufficient specifications in AI-based systems.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 7.7 (Selecting a Test Approach for an ML System)
* ISTQB CT-AI Syllabus v1.0, Section 9.6 (Experience-Based Testing of AI-Based Systems).

NEW QUESTION # 118
An ML engineer performing supervised learning needs to label images of football games based on the location of the football in the image. Which ONE of the below labeling approaches can be used?
  • A. Annotation
  • B. Augmentation
  • C. Internal
  • D. Benchmarking
Answer: A
Explanation:
Annotation is the correct labeling approach for supervised learning, as it involves manually labeling the images with the correct information, such as marking the location of the football in the image. This labeled data can then be used to train a machine learning model.

NEW QUESTION # 119
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION
  • A. Testing the API of the service powered by the ML model.
  • B. Testing the speed of the training of the model.
  • C. Testing the accuracy of the classification model.
  • D. Testing the speed of the prediction by the model.
Answer: B
Explanation:
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
* Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
* Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
* Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
* Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real- time applications.
References:
* ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.

NEW QUESTION # 120
Which of the following statements about explainable AI is correct?
Choose ONE option (1 out of 4)
  • A. According to The Royal Society, one reason for explainable AI is to increase user confidence in the system
  • B. According to The Royal Society, one reason for explainable AI is to eliminate the need for risk and vulnerability assessments
  • C. Explainability refers to how easily the algorithms and training data needed to create the model can be determined
  • D. Interpretability refers to how easily users can determine whether the result provided by the AI-based system is correct
Answer: A
Explanation:
Section2.10 - Explainability and Transparencyof the ISTQB CT-AI syllabus describes explainable AI as the ability of a system to provide human-understandable insight into its decisions. The syllabus referencesThe Royal Society's reportas a foundational source explaining why explainability is important. Among the stated motivations is the need toincrease user trust and confidencein AI systems by making their decisions understandable and justifiable. Therefore, OptionCdirectly reflects the syllabus content .
Option A is incorrect because interpretability doesnotrefer to determining correctness of outputs; rather, it refers to understandinghowthe model arrives at outputs. Option B incorrectly frames explainability as the ability to investigate algorithms or training data; explainability is aboutunderstanding the model's decision- making, not reverse engineering its components. Option D is incorrect because explainability doesnot eliminate the need for risk and vulnerability assessments; the syllabus clearly emphasizes that testing, risk assessment, and robustness checks remain critical even when a model is explainable.
Thus, the only statement consistent with the syllabus isOption C.

NEW QUESTION # 121
......
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